Analysis on Indian Premier League¶
By: Mayank Lohani¶
Dataset : https://www.kaggle.com/nowke9/ipldata¶
Introduction: Indian Premier League (better known as IPL) is a short format (T-20) Mens Cricket tournament that is hosted in India. It is currently presided over by the Board of Cricket Control of India (BCCI). The Tournament comprises of 8 teams who complete with each other in a league format, which is followed by a knockout Stage. Some of the Teams who Played previously are no more in action
(For e.g. Deccan Chargers, Kochi Tuskers, Gujrat Lions, Pune Warriors etc). Two teams have been banned for two seasons, but have been a part of the tournament for the rest.
DATA READING¶
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import plotly.express as px
deliveries=pd.read_csv('data/deliveries.csv',encoding='latin1')
print(f'Number of rows = {len(deliveries)}')
print(f'Number of columns = {len(deliveries.columns)}')
deliveries.head()
Out[1]:
In [2]:
matches=pd.read_csv('data/matches.csv',encoding='latin1')
print(f'Number of rows = {len(matches)}')
print(f'Number of columns = {len(matches.columns)}')
matches.head()
Out[2]:
Cleaning the inconsistencies¶
Keeping team names and venue names same and abbreviating them for readability
In [3]:
x=['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',
'Rising Pune Supergiant', 'Royal Challengers Bangalore',
'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',
'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',
'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants', 'Delhi Capitals']
y = ['SRH','MI','GL','RPS','RCB','KKR','DC','KXIP','CSK','RR','SRH','KTK','PW','RPS','DC']
matches.team1.replace({'Rising Pune Supergiants' : 'Rising Pune Supergiant'},regex=True,inplace=True)
matches.team2.replace({'Rising Pune Supergiants' : 'Rising Pune Supergiant'},regex=True,inplace=True)
matches.winner.replace({'Rising Pune Supergiants' : 'Rising Pune Supergiant'},regex=True,inplace=True)
matches.venue.replace({'Feroz Shah Kotla Ground':'Feroz Shah Kotla',
'M Chinnaswamy Stadium':'M. Chinnaswamy Stadium',
'MA Chidambaram Stadium, Chepauk':'M.A. Chidambaram Stadium',
'M. A. Chidambaram Stadium':'M.A. Chidambaram Stadium',
'Punjab Cricket Association IS Bindra Stadium, Mohali':'Punjab Cricket Association Stadium',
'Punjab Cricket Association Stadium, Mohali':'Punjab Cricket Association Stadium',
'IS Bindra Stadium':'Punjab Cricket Association Stadium',
'Rajiv Gandhi International Stadium, Uppal':'Rajiv Gandhi International Stadium',
'Rajiv Gandhi Intl. Cricket Stadium':'Rajiv Gandhi International Stadium'},regex=True,inplace=True)
matches.replace(x,y,inplace = True)
deliveries.replace(x,y,inplace = True)
In [4]:
# umpire3 contains all null values we can omit the column
matches.drop(['umpire3'], axis = 1, inplace = True)
Let's dive into Anlaysis¶
Total No. of matches till 2020
In [5]:
def annotation_plot(ax,w,h):
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
for p in ax.patches:
ax.annotate('{0:.1f}'.format(p.get_height()), (p.get_x()+w, p.get_height()+h))
total_matches = matches.groupby('season')['id'].count()
plt.figure(figsize=(10,5))
ax = sns.countplot("season", data = matches, palette='viridis')
plt.title('Total number of matches till 2020 (2008-2019)')
plt.ylabel('Number of Matches.')
annotation_plot(ax,0.08,1)
plt.show()
Total No. of wins by each team till 2020
In [6]:
plt.figure(figsize=(12,7))
ax = sns.countplot("winner", data = matches, order = matches['winner'].value_counts().index,palette='viridis')
plt.title("Total number of wins by each team till 2020")
plt.xticks(rotation=45, ha = 'right')
plt.ylabel('Number of matches')
annotation_plot(ax,0.08,1)
plt.show()
Year by Year Analysis till 2020
In [7]:
max_times_winner = matches.groupby('season')['winner'].value_counts()
groups = max_times_winner.groupby('season')
fig = plt.figure()
count = 1
for year, group in groups:
ax = fig.add_subplot(4,3,count)
ax.set_title(year)
ax = group[year].plot.bar(figsize = (10,15), width = 0.8)
count+=1;
plt.xlabel('')
plt.yticks([])
plt.ylabel('Matches Won')
total_of_matches = []
for i in ax.patches:
total_of_matches.append(i.get_height())
total = sum(total_of_matches)
for i in ax.patches:
ax.text(i.get_x()+0.2, i.get_height()-1.5,s= i.get_height(),color="black",fontweight='bold')
plt.tight_layout()
plt.show()
Top Cities that have hosted IPL matches
In [8]:
city_counts= matches.groupby('city').apply(lambda x:x['city'].count()).reset_index(name='Match Counts')
top_cities_order=city_counts.sort_values(by='Match Counts',ascending=False)
top_cities=top_cities_order[:10]
print('Top 10 Cities with the maximum number of Matches Played:\n',top_cities)
plt.figure(figsize=(7,7))
plt.pie(top_cities['Match Counts'],labels=top_cities['city'],autopct='%1.1f%%', startangle=30)
plt.axis('equal')
plt.title('Top Cities that have hosted IPL Matches',size=10)
Out[8]:
Analysis of Captain's Decision after winning toss till 2020
In [9]:
sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(18.5,8)
sns.countplot(matches['toss_winner'],order=matches['toss_winner'].value_counts().index,palette='Set2',hue=matches['toss_decision'])
plt.title('Decision',fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15,rotation=90)
plt.xlabel('Teams',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.legend(loc=5,fontsize=15)
plt.show()
Top 10 Batsmen in the Tournament till 2020
In [10]:
batting_tot=deliveries.groupby('batsman').apply(lambda x:np.sum(x['batsman_runs'])).reset_index(name='Runs')
batting_sorted=batting_tot.sort_values(by='Runs',ascending=False)
top_batsmen=batting_sorted[:10]
print('The Top 10 Batsmen in the Tournament are:\n',top_batsmen)
fig = px.bar(top_batsmen, x='batsman', y='Runs',
hover_data=['batsman'], color='Runs',title='Top 10 Batsmen in IPL- Seasons 2008-2019')
fig.show()
Kohli is our Star Batsman
Top 10 Bowler in the Tournament till 2020
In [11]:
bowling_wickets=deliveries[deliveries['dismissal_kind']!='run out']
bowling_tot=bowling_wickets.groupby('bowler').apply(lambda x:x['dismissal_kind'].dropna()).reset_index(name='Wickets')
bowling_wick_count=bowling_tot.groupby('bowler').count().reset_index()
bowling_top=bowling_wick_count.sort_values(by='Wickets',ascending=False)
top_bowlers=bowling_top.loc[:,['bowler','Wickets']][0:10]
print('The Top 10 Bowler in the Tournament are:\n',top_bowlers)
fig = px.bar(top_bowlers, x='bowler', y='Wickets',hover_data=['bowler'], color='Wickets',title='Top 10 Bowler in IPL- Seasons 2008-2019')
fig.show()
Malinga is our Star Bowler
The Head-to-Head Performace Matrix of Teams in IPL till 2020
In [12]:
Current_teams=['SRH','MI','GL','RPS','RCB','KKR','DC','KXIP','CSK','RR','SRH','KTK','PW','RPS','DC']
team_1_filter=matches[matches.team1.isin(Current_teams)]
team_2_filter=team_1_filter[team_1_filter.team2.isin(Current_teams)]
teams_filter=team_2_filter[team_2_filter.winner.isin(Current_teams)]
head_to_head_matches=teams_filter.groupby(['team1','team2','winner']).apply(lambda x:x['winner'].count()).reset_index(name='Winning Counts')
head_to_head_matches['Game']=head_to_head_matches['team1']+' vs. '+head_to_head_matches['team2']
head_to_head_matches.loc[:,['Game','winner','Winning Counts']]
heatmap1_data = pd.pivot_table(head_to_head_matches, values='Winning Counts',
index=['Game'],
columns='winner')
fig = plt.figure()
fig, ax = plt.subplots(1,1, figsize=(12,15))
g=sns.heatmap(heatmap1_data,annot=True, cmap="GnBu",fmt='g')
ax.set_title('The Head-to-Head Performace Matrix of Teams in IPL',size=20)
ax.set_xlabel('IPL Teams',size=15)
ax.set_ylabel('Match',size=15)
Out[12]:
Biggest win by runs each season
In [13]:
s=2008
season=[]
win_by_runs_max=[]
while s<2020:
season.append(s)
win_by_runs_max.append(matches[matches['season']==s]['win_by_runs'].max())
s+=1
fig=plt.gcf()
fig.set_size_inches(18.5,10.5)
plt.plot(season,win_by_runs_max)
plt.scatter(season,win_by_runs_max)
count=0
while count<12:
plt.text(season[count]+0.1,win_by_runs_max[count],str(win_by_runs_max[count]),size=14)
count+=1
plt.xticks(range(2008,2020),fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel('Season',fontsize=14)
plt.ylabel('Runs',fontsize=14)
plt.title('Biggest win by runs per season',fontsize=14)
plt.show()
Season wise match summary of matches won by runs
In [14]:
fig=plt.gcf()
fig.set_size_inches(18.5,10.5)
sns.swarmplot(matches['season'],matches[matches['win_by_runs']!=0]['win_by_runs'],s=10)
plt.xticks(rotation=90,fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel('Season',fontsize=14)
plt.ylabel('Runs',fontsize=14)
plt.title('Season wise match summary of matches won by runs',fontsize=14)
plt.show()
All Set for MI vs CSK Analysis
In [15]:
mi_csk=matches[np.logical_or(
np.logical_and(matches['team1']=='MI',matches['team2']=='CSK'),
np.logical_and(matches['team1']=='CSK',matches['team2']=='MI'))]
MI vs CSK head to head
In [16]:
sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(10,8)
sns.countplot(mi_csk['winner'],order=mi_csk['winner'].value_counts().index)
plt.text(-0.1,15,str(mi_csk['winner'].value_counts()['MI']),size=29,color='white')
plt.text(0.9,9,str(mi_csk['winner'].value_counts()['CSK']),size=29,color='white')
plt.xlabel('Winner',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.yticks(fontsize=0)
plt.title('MI vs CSK - head to head')
plt.show()
Toss decision statistics for all MIvCSK matches - Venue wise
In [17]:
sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(18.5,8)
sns.countplot(mi_csk['venue'],order=mi_csk['venue'].value_counts().index,palette='Set2',hue=matches['toss_decision'])
plt.title('Toss decision at each venue in MI vs CSK matches',fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15,rotation=90)
plt.xlabel('Venue',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.legend(loc=5,fontsize=15)
plt.show()
Decision upon winning the toss by both teams
In [18]:
sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(18.5,8)
sns.countplot(mi_csk['toss_winner'],order=mi_csk['toss_winner'].value_counts().index,palette='Set2',hue=matches['toss_decision'])
plt.title('Toss decision statistics for both team',fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.xlabel('Toss winner',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.text(-0.25,6,str(int((7/15)*100)+1)+'%',fontsize=29)
plt.text(0.15,7,str(int((8/15)*100))+'%',fontsize=29)
plt.text(0.75,7,str(int((8/13)*100)+1)+'%',fontsize=29)
plt.text(1.15,4,str(int((5/13)*100))+'%',fontsize=29)
plt.legend(['Field first','Bat first'],loc='best',fontsize=15)
plt.show()
- MI tends to bat first (53% of the times)
- CSK tends to bowl first (62% of the times)
MI vs CSK - Best performers
In [19]:
sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(18.5,8)
sns.countplot(mi_csk['player_of_match'],order=mi_csk['player_of_match'].value_counts().index,palette='Set2')
plt.title('All man of the match awards in MI-CSK games',fontsize=15)
plt.yticks([1,2,3],[1,2,3],fontsize=15)
plt.xticks(fontsize=15,rotation=90)
plt.xlabel('Man of the match',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.show()
